Overview

Dataset statistics

Number of variables12
Number of observations150000
Missing cells33655
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 MiB
Average record size in memory96.0 B

Variable types

Numeric11
Categorical1

Alerts

SeriousDlqin2yrs is highly imbalanced (64.6%)Imbalance
MonthlyIncome has 29731 (19.8%) missing valuesMissing
NumberOfDependents has 3924 (2.6%) missing valuesMissing
RevolvingUtilizationOfUnsecuredLines is highly skewed (γ1 = 97.63157449)Skewed
NumberOfTime30-59DaysPastDueNotWorse is highly skewed (γ1 = 22.59710756)Skewed
DebtRatio is highly skewed (γ1 = 95.15779287)Skewed
MonthlyIncome is highly skewed (γ1 = 114.0403179)Skewed
NumberOfTimes90DaysLate is highly skewed (γ1 = 23.08734547)Skewed
NumberOfTime60-89DaysPastDueNotWorse is highly skewed (γ1 = 23.33174312)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
RevolvingUtilizationOfUnsecuredLines has 10878 (7.3%) zerosZeros
NumberOfTime30-59DaysPastDueNotWorse has 126018 (84.0%) zerosZeros
DebtRatio has 4113 (2.7%) zerosZeros
MonthlyIncome has 1634 (1.1%) zerosZeros
NumberOfOpenCreditLinesAndLoans has 1888 (1.3%) zerosZeros
NumberOfTimes90DaysLate has 141662 (94.4%) zerosZeros
NumberRealEstateLoansOrLines has 56188 (37.5%) zerosZeros
NumberOfTime60-89DaysPastDueNotWorse has 142396 (94.9%) zerosZeros
NumberOfDependents has 86902 (57.9%) zerosZeros

Reproduction

Analysis started2023-09-12 16:22:55.446158
Analysis finished2023-09-12 16:23:41.396429
Duration45.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct150000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75000.5
Minimum1
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:41.689897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7500.95
Q137500.75
median75000.5
Q3112500.25
95-th percentile142500.05
Maximum150000
Range149999
Interquartile range (IQR)74999.5

Descriptive statistics

Standard deviation43301.415
Coefficient of variation (CV)0.57734834
Kurtosis-1.2
Mean75000.5
Median Absolute Deviation (MAD)37500
Skewness0
Sum1.1250075 × 1010
Variance1.8750125 × 109
MonotonicityStrictly increasing
2023-09-12T16:23:42.251625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
100013 1
 
< 0.1%
99997 1
 
< 0.1%
99998 1
 
< 0.1%
99999 1
 
< 0.1%
100000 1
 
< 0.1%
100001 1
 
< 0.1%
100002 1
 
< 0.1%
100003 1
 
< 0.1%
100004 1
 
< 0.1%
Other values (149990) 149990
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
150000 1
< 0.1%
149999 1
< 0.1%
149998 1
< 0.1%
149997 1
< 0.1%
149996 1
< 0.1%
149995 1
< 0.1%
149994 1
< 0.1%
149993 1
< 0.1%
149992 1
< 0.1%
149991 1
< 0.1%

SeriousDlqin2yrs
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.3 MiB
0
139974 
1
 
10026

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Length

2023-09-12T16:23:42.726344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T16:23:42.993221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 150000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139974
93.3%
1 10026
 
6.7%

RevolvingUtilizationOfUnsecuredLines
Real number (ℝ)

SKEWED  ZEROS 

Distinct125728
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0484381
Minimum0
Maximum50708
Zeros10878
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:43.226324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.029867442
median0.15418074
Q30.55904625
95-th percentile0.9999999
Maximum50708
Range50708
Interquartile range (IQR)0.52917881

Descriptive statistics

Standard deviation249.75537
Coefficient of variation (CV)41.29254
Kurtosis14544.713
Mean6.0484381
Median Absolute Deviation (MAD)0.14832535
Skewness97.631574
Sum907265.71
Variance62377.745
MonotonicityNot monotonic
2023-09-12T16:23:43.526897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10878
 
7.3%
0.9999999 10256
 
6.8%
1 17
 
< 0.1%
0.9500998 8
 
< 0.1%
0.007984032 6
 
< 0.1%
0.954091816 6
 
< 0.1%
0.71314741 6
 
< 0.1%
0.796407186 5
 
< 0.1%
0.988023952 5
 
< 0.1%
0.994011976 5
 
< 0.1%
Other values (125718) 128808
85.9%
ValueCountFrequency (%)
0 10878
7.3%
8.37 × 10-61
 
< 0.1%
9.93 × 10-61
 
< 0.1%
1.25 × 10-51
 
< 0.1%
1.43 × 10-51
 
< 0.1%
1.49 × 10-51
 
< 0.1%
1.51 × 10-51
 
< 0.1%
1.6 × 10-51
 
< 0.1%
1.64 × 10-51
 
< 0.1%
1.87 × 10-51
 
< 0.1%
ValueCountFrequency (%)
50708 1
< 0.1%
29110 1
< 0.1%
22198 1
< 0.1%
22000 1
< 0.1%
20514 1
< 0.1%
18300 1
< 0.1%
17441 1
< 0.1%
13930 1
< 0.1%
13498 1
< 0.1%
13400 1
< 0.1%

age
Real number (ℝ)

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.295207
Minimum0
Maximum109
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:43.831748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q141
median52
Q363
95-th percentile78
Maximum109
Range109
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.771866
Coefficient of variation (CV)0.28247074
Kurtosis-0.49466883
Mean52.295207
Median Absolute Deviation (MAD)11
Skewness0.18899455
Sum7844281
Variance218.20802
MonotonicityNot monotonic
2023-09-12T16:23:44.129861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 3837
 
2.6%
48 3806
 
2.5%
50 3753
 
2.5%
47 3719
 
2.5%
63 3719
 
2.5%
46 3714
 
2.5%
53 3648
 
2.4%
51 3627
 
2.4%
52 3609
 
2.4%
56 3589
 
2.4%
Other values (76) 112979
75.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
21 183
 
0.1%
22 434
 
0.3%
23 641
 
0.4%
24 816
0.5%
25 953
0.6%
26 1193
0.8%
27 1338
0.9%
28 1560
1.0%
29 1702
1.1%
ValueCountFrequency (%)
109 2
 
< 0.1%
107 1
 
< 0.1%
105 1
 
< 0.1%
103 3
 
< 0.1%
102 3
 
< 0.1%
101 3
 
< 0.1%
99 9
< 0.1%
98 6
 
< 0.1%
97 17
< 0.1%
96 18
< 0.1%

NumberOfTime30-59DaysPastDueNotWorse
Real number (ℝ)

SKEWED  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42103333
Minimum0
Maximum98
Zeros126018
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:44.388128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1927813
Coefficient of variation (CV)9.9583119
Kurtosis522.37654
Mean0.42103333
Median Absolute Deviation (MAD)0
Skewness22.597108
Sum63155
Variance17.579415
MonotonicityNot monotonic
2023-09-12T16:23:44.621726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 126018
84.0%
1 16033
 
10.7%
2 4598
 
3.1%
3 1754
 
1.2%
4 747
 
0.5%
5 342
 
0.2%
98 264
 
0.2%
6 140
 
0.1%
7 54
 
< 0.1%
8 25
 
< 0.1%
Other values (6) 25
 
< 0.1%
ValueCountFrequency (%)
0 126018
84.0%
1 16033
 
10.7%
2 4598
 
3.1%
3 1754
 
1.2%
4 747
 
0.5%
5 342
 
0.2%
6 140
 
0.1%
7 54
 
< 0.1%
8 25
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
98 264
0.2%
96 5
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 4
 
< 0.1%
9 12
 
< 0.1%
8 25
 
< 0.1%
7 54
 
< 0.1%
6 140
0.1%

DebtRatio
Real number (ℝ)

SKEWED  ZEROS 

Distinct114194
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.00508
Minimum0
Maximum329664
Zeros4113
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:44.909873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004329004
Q10.17507383
median0.36650784
Q30.86825377
95-th percentile2449
Maximum329664
Range329664
Interquartile range (IQR)0.69317994

Descriptive statistics

Standard deviation2037.8185
Coefficient of variation (CV)5.772774
Kurtosis13734.289
Mean353.00508
Median Absolute Deviation (MAD)0.2457228
Skewness95.157793
Sum52950761
Variance4152704.3
MonotonicityNot monotonic
2023-09-12T16:23:45.204552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4113
 
2.7%
1 229
 
0.2%
4 174
 
0.1%
2 170
 
0.1%
3 162
 
0.1%
5 143
 
0.1%
9 125
 
0.1%
10 117
 
0.1%
7 115
 
0.1%
13 114
 
0.1%
Other values (114184) 144538
96.4%
ValueCountFrequency (%)
0 4113
2.7%
2.6 × 10-51
 
< 0.1%
3.69 × 10-51
 
< 0.1%
3.93 × 10-51
 
< 0.1%
6.62 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8 × 10-51
 
< 0.1%
8.57 × 10-51
 
< 0.1%
9.09 × 10-51
 
< 0.1%
9.15 × 10-51
 
< 0.1%
ValueCountFrequency (%)
329664 1
< 0.1%
326442 1
< 0.1%
307001 1
< 0.1%
220516 1
< 0.1%
168835 1
< 0.1%
110952 1
< 0.1%
106885 1
< 0.1%
101320 1
< 0.1%
61907 1
< 0.1%
61106.5 1
< 0.1%

MonthlyIncome
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13594
Distinct (%)11.3%
Missing29731
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean6670.2212
Minimum0
Maximum3008750
Zeros1634
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:45.922578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1300
Q13400
median5400
Q38249
95-th percentile14587.6
Maximum3008750
Range3008750
Interquartile range (IQR)4849

Descriptive statistics

Standard deviation14384.674
Coefficient of variation (CV)2.1565513
Kurtosis19504.705
Mean6670.2212
Median Absolute Deviation (MAD)2317
Skewness114.04032
Sum8.0222084 × 108
Variance2.0691885 × 108
MonotonicityNot monotonic
2023-09-12T16:23:46.237304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 2757
 
1.8%
4000 2106
 
1.4%
6000 1934
 
1.3%
3000 1758
 
1.2%
0 1634
 
1.1%
2500 1551
 
1.0%
10000 1466
 
1.0%
3500 1360
 
0.9%
4500 1226
 
0.8%
7000 1223
 
0.8%
Other values (13584) 103254
68.8%
(Missing) 29731
 
19.8%
ValueCountFrequency (%)
0 1634
1.1%
1 605
 
0.4%
2 6
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
3008750 1
< 0.1%
1794060 1
< 0.1%
1560100 1
< 0.1%
1072500 1
< 0.1%
835040 1
< 0.1%
730483 1
< 0.1%
702500 1
< 0.1%
699530 1
< 0.1%
649587 1
< 0.1%
629000 1
< 0.1%

NumberOfOpenCreditLinesAndLoans
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.45276
Minimum0
Maximum58
Zeros1888
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:46.541854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile18
Maximum58
Range58
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.145951
Coefficient of variation (CV)0.60878944
Kurtosis3.0910667
Mean8.45276
Median Absolute Deviation (MAD)3
Skewness1.2153138
Sum1267914
Variance26.480812
MonotonicityNot monotonic
2023-09-12T16:23:46.849144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 13614
 
9.1%
7 13245
 
8.8%
5 12931
 
8.6%
8 12562
 
8.4%
4 11609
 
7.7%
9 11355
 
7.6%
10 9624
 
6.4%
3 9058
 
6.0%
11 8321
 
5.5%
12 7005
 
4.7%
Other values (48) 40676
27.1%
ValueCountFrequency (%)
0 1888
 
1.3%
1 4438
 
3.0%
2 6666
4.4%
3 9058
6.0%
4 11609
7.7%
5 12931
8.6%
6 13614
9.1%
7 13245
8.8%
8 12562
8.4%
9 11355
7.6%
ValueCountFrequency (%)
58 1
 
< 0.1%
57 2
 
< 0.1%
56 2
 
< 0.1%
54 4
< 0.1%
53 1
 
< 0.1%
52 3
< 0.1%
51 2
 
< 0.1%
50 2
 
< 0.1%
49 4
< 0.1%
48 6
< 0.1%

NumberOfTimes90DaysLate
Real number (ℝ)

SKEWED  ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26597333
Minimum0
Maximum98
Zeros141662
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:47.137422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1693038
Coefficient of variation (CV)15.675646
Kurtosis537.73894
Mean0.26597333
Median Absolute Deviation (MAD)0
Skewness23.087345
Sum39896
Variance17.383094
MonotonicityNot monotonic
2023-09-12T16:23:47.383546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 141662
94.4%
1 5243
 
3.5%
2 1555
 
1.0%
3 667
 
0.4%
4 291
 
0.2%
98 264
 
0.2%
5 131
 
0.1%
6 80
 
0.1%
7 38
 
< 0.1%
8 21
 
< 0.1%
Other values (9) 48
 
< 0.1%
ValueCountFrequency (%)
0 141662
94.4%
1 5243
 
3.5%
2 1555
 
1.0%
3 667
 
0.4%
4 291
 
0.2%
5 131
 
0.1%
6 80
 
0.1%
7 38
 
< 0.1%
8 21
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
98 264
0.2%
96 5
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 4
 
< 0.1%
12 2
 
< 0.1%
11 5
 
< 0.1%
10 8
 
< 0.1%
9 19
 
< 0.1%

NumberRealEstateLoansOrLines
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.01824
Minimum0
Maximum54
Zeros56188
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:47.646354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum54
Range54
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.129771
Coefficient of variation (CV)1.1095331
Kurtosis60.476808
Mean1.01824
Median Absolute Deviation (MAD)1
Skewness3.482484
Sum152736
Variance1.2763825
MonotonicityNot monotonic
2023-09-12T16:23:47.928228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 56188
37.5%
1 52338
34.9%
2 31522
21.0%
3 6300
 
4.2%
4 2170
 
1.4%
5 689
 
0.5%
6 320
 
0.2%
7 171
 
0.1%
8 93
 
0.1%
9 78
 
0.1%
Other values (18) 131
 
0.1%
ValueCountFrequency (%)
0 56188
37.5%
1 52338
34.9%
2 31522
21.0%
3 6300
 
4.2%
4 2170
 
1.4%
5 689
 
0.5%
6 320
 
0.2%
7 171
 
0.1%
8 93
 
0.1%
9 78
 
0.1%
ValueCountFrequency (%)
54 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
26 1
 
< 0.1%
25 3
< 0.1%
23 2
< 0.1%
21 1
 
< 0.1%
20 2
< 0.1%
19 2
< 0.1%
18 2
< 0.1%

NumberOfTime60-89DaysPastDueNotWorse
Real number (ℝ)

SKEWED  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24038667
Minimum0
Maximum98
Zeros142396
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:48.180022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1551794
Coefficient of variation (CV)17.285399
Kurtosis545.68274
Mean0.24038667
Median Absolute Deviation (MAD)0
Skewness23.331743
Sum36058
Variance17.265516
MonotonicityNot monotonic
2023-09-12T16:23:48.411145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 142396
94.9%
1 5731
 
3.8%
2 1118
 
0.7%
3 318
 
0.2%
98 264
 
0.2%
4 105
 
0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%
7 9
 
< 0.1%
96 5
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
0 142396
94.9%
1 5731
 
3.8%
2 1118
 
0.7%
3 318
 
0.2%
4 105
 
0.1%
5 34
 
< 0.1%
6 16
 
< 0.1%
7 9
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
98 264
0.2%
96 5
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 9
 
< 0.1%
6 16
 
< 0.1%
5 34
 
< 0.1%
4 105
 
0.1%
3 318
0.2%

NumberOfDependents
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing3924
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean0.75722227
Minimum0
Maximum20
Zeros86902
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-09-12T16:23:48.646809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1150861
Coefficient of variation (CV)1.4726007
Kurtosis3.0016568
Mean0.75722227
Median Absolute Deviation (MAD)0
Skewness1.5882424
Sum110612
Variance1.2434169
MonotonicityNot monotonic
2023-09-12T16:23:48.881400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 86902
57.9%
1 26316
 
17.5%
2 19522
 
13.0%
3 9483
 
6.3%
4 2862
 
1.9%
5 746
 
0.5%
6 158
 
0.1%
7 51
 
< 0.1%
8 24
 
< 0.1%
10 5
 
< 0.1%
Other values (3) 7
 
< 0.1%
(Missing) 3924
 
2.6%
ValueCountFrequency (%)
0 86902
57.9%
1 26316
 
17.5%
2 19522
 
13.0%
3 9483
 
6.3%
4 2862
 
1.9%
5 746
 
0.5%
6 158
 
0.1%
7 51
 
< 0.1%
8 24
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
13 1
 
< 0.1%
10 5
 
< 0.1%
9 5
 
< 0.1%
8 24
 
< 0.1%
7 51
 
< 0.1%
6 158
 
0.1%
5 746
 
0.5%
4 2862
 
1.9%
3 9483
6.3%

Interactions

2023-09-12T16:23:35.930379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:02.781409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:05.803481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:09.017666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:12.112399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:16.153736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:19.394583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:22.370769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:25.432487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:29.539493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:32.940936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:36.219352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:03.055599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:06.074606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:09.286809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:12.432799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:16.423585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:19.659709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:22.641889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:25.849402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:29.807458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:33.221945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:36.499816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:03.348606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:06.351272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:09.555376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:12.859540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:16.705417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:19.941517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:22.910810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:26.218528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:30.078989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:33.505530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:36.764040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:03.609208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:06.628213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:09.813271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:13.264974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:16.965406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:20.193394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:23.184831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:26.614172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:30.358536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:33.769943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:37.046412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:03.879493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:06.885531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:10.068352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:13.690287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:17.237644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:20.453270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:23.444512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:27.014262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:30.606458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:34.035293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:37.318619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:04.149118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:07.365469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:10.339784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:14.116892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:17.500955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:20.719357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:23.713136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:27.423900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:31.190769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:34.307099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:37.612680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:04.431786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:07.643286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:10.629026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:14.570560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:18.042884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:20.998399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:23.998517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:27.823098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:31.471624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:34.587979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:37.897027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:04.701123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:07.919665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:10.886744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:15.022389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:18.314325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:21.270107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:24.281141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:28.203892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:31.731409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:34.864717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:38.175584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:04.979909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:08.181242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:11.151728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:15.358680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:18.578922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:21.533587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:24.534955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:28.584123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:31.983763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:35.117677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:38.447447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:05.243004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:08.452090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:11.409442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:15.610814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:18.843996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:21.801395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:24.792971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:28.987706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:32.240086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:35.376028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:38.728227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:05.521299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:08.724138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:11.689923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:15.872809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:19.116380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:22.077523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:25.070462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:29.258091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:32.648166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-12T16:23:35.646944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-12T16:23:49.138283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0RevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependentsSeriousDlqin2yrs
Unnamed: 01.000-0.0040.0040.001-0.0010.0010.005-0.005-0.0010.001-0.0000.006
RevolvingUtilizationOfUnsecuredLines-0.0041.000-0.2780.2340.077-0.078-0.0870.238-0.0270.1880.1180.000
age0.004-0.2781.000-0.0950.0290.1350.158-0.1040.054-0.085-0.2280.116
NumberOfTime30-59DaysPastDueNotWorse0.0010.234-0.0951.0000.038-0.0150.0640.2530.0220.2800.0710.084
DebtRatio-0.0010.0770.0290.0381.000-0.1310.227-0.0320.4000.001-0.0380.000
MonthlyIncome0.001-0.0780.135-0.015-0.1311.0000.312-0.0880.391-0.0530.2040.000
NumberOfOpenCreditLinesAndLoans0.005-0.0870.1580.0640.2270.3121.000-0.1350.473-0.0480.1000.049
NumberOfTimes90DaysLate-0.0050.238-0.1040.253-0.032-0.088-0.1351.000-0.1010.3210.0300.085
NumberRealEstateLoansOrLines-0.001-0.0270.0540.0220.4000.3910.473-0.1011.000-0.0440.1660.033
NumberOfTime60-89DaysPastDueNotWorse0.0010.188-0.0850.2800.001-0.053-0.0480.321-0.0441.0000.0350.082
NumberOfDependents-0.0000.118-0.2280.071-0.0380.2040.1000.0300.1660.0351.0000.031
SeriousDlqin2yrs0.0060.0000.1160.0840.0000.0000.0490.0850.0330.0820.0311.000

Missing values

2023-09-12T16:23:39.160728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-12T16:23:40.111789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-12T16:23:41.042000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0SeriousDlqin2yrsRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependents
0110.7661274520.8029829120.0130602.0
1200.9571514000.1218762600.040001.0
2300.6581803810.0851133042.021000.0
3400.2338103000.0360503300.050000.0
4500.9072394910.02492663588.070100.0
5600.2131797400.3756073500.030101.0
6700.3056825705710.000000NaN80300.0
7800.7544643900.2099403500.080000.0
8900.11695127046.000000NaN2000NaN
91000.1891695700.60629123684.090402.0
Unnamed: 0SeriousDlqin2yrsRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependents
14999014999100.0555184600.6097794335.070102.0
14999114999200.1041125900.47765810316.0100200.0
14999214999300.8719765004132.000000NaN110103.0
14999314999401.0000002200.000000820.010000.0
14999414999500.3857425000.4042933400.070000.0
14999514999600.0406747400.2251312100.040100.0
14999614999700.2997454400.7165625584.040102.0
14999714999800.2460445803870.000000NaN180100.0
14999814999900.0000003000.0000005716.040000.0
14999915000000.8502836400.2499088158.080200.0